Lead Software Engineer - Full Stack

hackajob is collaborating with J.P. Morgan to connect them with exceptional professionals for this role.

JOB DESCRIPTION

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.

As a Lead Software Engineer
- Full Stack at JPMorgan Chase within the Enterprise Technology
- Network Services Team, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm's business objectives. This role is hands-on and suited for seasoned full stack engineers who can own end-to-end delivery - from system design through implementation, deployment on Kubernetes, and production operations (monitoring, troubleshooting, and performance tuning).

Job responsibilities

• Executes creative software solutions, design, development, and technical troubleshooting with the ability to think beyond routine or conventional approaches to build solutions or break down technical problems
• Owns end-to-end full stack delivery across; Frontend (React/Type Script), Backend (Python services), Data/Workflow Services (Apache Airflow - DAG design) and Database (Cockroach DB - data modeling)
• Designs and delivers scalable, highly available services and user experiences for large-scale applications; drives architecture decisions that improve throughput, latency, reliability, and operability
• Builds and maintains cloud-native deployments on Kubernetes, including configuration, scaling strategies, and operational readiness (health checks, rollouts, rollback strategies, capacity considerations)
• Drives monitoring, observability, and performance tuning across the stack using tools such as Splunk and Grafana (and related logging/metrics/tracing patterns); leads root-cause analysis and remediation
• Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team
• Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation
• Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems
• Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 5+ years applied experience
• Demonstrated full stack engineering capability (frontend + backend) and ability to deliver independently across the SDLC
• Strong proficiency in Python and experience building microservices and automation frameworks; working knowledge of JavaScript for tooling/UI/integrations and strong React experience building production-grade web applications (performance, usability, maintainability)
• Hands-on experience deploying and operating workloads on Kubernetes (deployments, services, scaling, configuration, troubleshooting) and hands-on experience with API gateways, Kafka, and event-driven architectures
• Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
• Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices
• Experience with workflow orchestration using Apache Airflow (DAG authoring, dependency design, failure handling, and operational support) and experience with distributed databases / SQL data stores; working knowledge of Cockroach DB or similar distributed SQL systems (schema design, query performance, reliability considerations)
• Proven ability to support observability and operations, including log/metric-based troubleshooting and performance tuning using tools such as Splunk and Grafana
• Experience developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages and database…